The application of Artificial Intelligence in healthcare has held great promise for a number of years. One of the obvious applications is the ability to identify patient populations that represent the proverbial needle in a haystack. Examples of this include undiagnosed rare disease patients, patients soon to fail a line of therapy, patients most at risk of adherence or patients who fit the profile for a clinical trial. However, as with many new technologies, the realization of this promise has been disappointingly slow.

The challenges that have hindered widespread adoption include:

  • Notoriously dirty data – duplicate entries, missing data, incomplete coding, etc
  • Complex interactions among multiple conditions that are also temporally sensitive
  • Identification and ranking of contributing conditions
  • Translation of results to real world business objectives has developed the first scalable AI platform that solves all of these challenges. Notably, this is not a custom solution that requires an extended consulting engagement; rather, it is a platform that delivers patient models that are better, faster and in a form that answers your business challenges, and all done with a higher ROI.

The AI Platform includes a unique combination of technologies each designed to solve one of the Health Data specific challenges:

  • Neural Nets and Deep Learning – These cutting edge techniques uncover the deep underlying connections between any number of medical conditions, arrayed across any time span. It is these previously-unknown interactions that are most predictive in applications like finding undiagnosed patients.
  • Natural Language Processing – NLP helps solve the problem of incomplete, missing or unspecified coding. For example NLP lets us see ICD-10 150.8, Unspecified Heart Failure, and using NLP with emphasis on relationship extraction between co-morbid or concomitant codes, this unspecified code can be correctly re-mapped and indexed for level of severity, greatly enhancing the specificity of the model.   
  • Ensemble of algorithms – Models that are carefully engineered to be as independent as possible provide an average result, more precise than any individual result. This approach identifies the key features that drive the model and their relative importance, providing insights on the most predictive triggers that can inform other business decisions around the therapy.
  • Non-parametric models tuned to business objective – Traditional models measure the propensity of a population having the desired characteristic, with populations scored on a 0 to 1 scale. However, saying a population is 0.9 vs 0.8 provides no insight into the tradeoffs a business owner is faced with when trying to pick the correct population to target.’s models are always tuned to answer a business question, such as “What is the correct population to target that results in a number of patients where a 20% conversion rate would result in a program ROI of 100%?”

To learn more about the how’s AI Platform can help find, activate and convert your ideal patient and HCP populations, contact us at [email protected]